{"id":"W4414621284","doi":"","title":"Private Rate-Constrained Optimization with Applications to Fair Learning","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canada Research Chairs; University of Toronto","funders":"","keywords":"Differential privacy; Convergence (economics); Empirical risk minimization; Constraint (computer-aided design); Minification; Dual (grammatical number); Optimization problem; Rate of convergence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007328153,0.001671431,0.002080812,0.001241415,0.001156499,0.003071788,0.002270778,0.002520718,0.008638361],"category_scores_gemma":[0.0279451,0.0006839034,0.0009150203,0.002185246,0.004017631,0.004554583,0.004346138,0.005330321,0.001081219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003455444,"about_ca_system_score_gemma":0.002966479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002409532,"about_ca_topic_score_gemma":0.002083938,"domain_scores_codex":[0.9974971,0.001349206,0.00007390512,0.000360241,0.0004843274,0.0002350799],"domain_scores_gemma":[0.9871863,0.01047924,0.0004838241,0.0008183027,0.0006551492,0.0003771695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001144291,0.00009946821,0.000215326,0.0001030567,0.00003473489,0.00006524043,0.00008461488,0.2992655,0.0005849724,0.6625975,0.0044233,0.03241197],"study_design_scores_gemma":[0.00001699831,0.00001480139,0.0000278666,0.00001415953,0.000005147954,0.00001453677,0.000007962214,0.6393115,0.0001989515,0.3593816,0.0009961771,0.00001035156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006095314,0.000661699,0.9837034,0.001627817,0.0001534525,0.00005812629,0.00008258229,0.0001382845,0.007479331],"genre_scores_gemma":[0.6145707,0.002768299,0.3461428,0.0007853392,0.001131924,0.000497595,0.0002356645,0.0004371029,0.03343073],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008638361,"threshold_uncertainty_score":0.03875542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134586674863861,"score_gpt":0.2596708159247941,"score_spread":0.2383249491761555,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}